Uncovering the context-dependent complexity of neural RNA modifications Brain development requires a delicate balance between neural progenitor proliferation and differentiation. Loss of this balance has been implicated in autism, anxiety and depression. Regulation of neural progenitor proliferation and differentiation occurs at all levels of gene expression, including messenger RNA decay and translation. The molecular mechanisms that control mRNA decay and translation are varied, but recent discoveries demonstrate that nucleotide modifications, the ?epitranscriptome?, are a common determinant of mRNA fates. The most abundant modification in human mRNA, N6-methyladenosine (m6A), affects mRNA decay and translation. While progress has been made in determining the functional significance of m6A, a major question remains unanswered: how do m6A modifications and their effects on metabolism vary according to cell type in vivo? Our team is uniquely positioned to address this question. We recently developed a method to purify cell type-specific RNAs from the Drosophila brain and this method can be paired with techniques that map m6A at nucleotide resolution (miCLIP), measure mRNA decay (EC-tag pulse/chase) and measure ribosome dynamics (5PSeq). We will use these novel experimental approaches to uncover the complexities of the m6A epitranscriptome in the Drosophila brain. The m6A methyltransferase complex is enriched throughout the Drosophila nervous system, but the distribution of m6A across mRNAs and the effects of m6A on mRNA decay and translation are unknown. First we will use EC-tagging coupled with miCLIP (EC- miCLIP) to generate m6A mRNA maps from whole larvae and compare these data to m6A maps generated from neural progenitors and differentiated neurons. The accuracy of our m6A mapping will be confirmed by performing EC-miCLIP in Drosophila that lack the m6A methyltransferase Ime4. These experiments will reveal neural cell type-specific patterns of m6A modification. Next we will measure mRNA decay and ribosome dynamics in wildtype and Ime4 mutant neural progenitors and pair these data with the site-specific m6A maps. These experiments will identify the metabolic consequences of different types of m6A modification. This work will significantly advance our understanding of the brain epitranscriptome and establish a powerful new method for identifying cell type-specific RNA modifications. This will lay the foundation for wide-ranging future epitranscriptome studies since EC-tagging may be applied to other model organisms and all types of RNA.